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Conventional thermal power system-based units and its participation schedule known as unit commitment problem (UCP) is a significant and stimulating undertaking of allocating generated power among the dedicated units subject to numerous restrictions above a scheduled time prospect to obtain the slightest generation cost. This problem becomes further more complex by increasing the size of the power system. Since unit commitment problem is link optimization problem as it has both binary and continuous variable that is why it is most challenging problem to solve. In this paper, a recently invented optimizer sine–cosine is used to solve unit commitment problem. Sine cosine algorithm (SCA) is an innovative population centered optimization algorithm that has been used for solving the unit commitment optimization problems bounded by some constraints centered on the concept of a mathematical model of the sine and cosine functions. This paper offers the solution of unit commitment optimization problems of the electric power system by using the SCA, as UCP is linked optimization as it has both binary and continuous variables, the strategy adopted to tackle both variables is different. In this paper, proposed sine cosine algorithm searches allocation of generators (units that participate in generation to take upload) and once units are decided, allocation of generations (economic load dispatch) is done by mixed integer quadratic programming. The feasibility and efficacy of operation of SCA algorithm are verified for small- and medium-power systems, in which results for 4 unit, 5 unit, 6 unit, 7 unit, 10 units, 19 unit, 20 unit and 40 units are evaluated. The 10 generating units are evaluated with 5% and 10% spinning reserve. The results obviously show that the suggested method gives the superior type of solutions as compared to other algorithms. [ABSTRACT FROM AUTHOR]

This study proposes a data-driven distributionally robust framework for unit commitment based on Wasserstein metric considering the wind power generation forecasting errors. The objective of the constructed model is to minimise the expected operating cost, including the generating cost, start-up and shut-down costs, and also the reserve cost, which overcomes the shortcomings of the conventional model without optimising the reserve capacity. What is more important, different from the conventional robust optimisation methods, wind power big data is fully utilised in this model to construct the ambiguity set without any presumption about its probability distribution. This is realised by Wasserstein ball with an empirical distribution as the centre. Thus, the proposed robust model is actually data-driven and can immunise the solutions against the worst-case distribution in the ambiguity set. In addition, the scale of the historical data is very critical for this method, the larger the scale is, the smaller the ambiguity set is and the less conservative the result is. Numerical results and Monte Carlo simulations on a real 75-bus system demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Highlights • Existing electricity markets, based on energy, are inefficient. • We compare power-based scheduling to the current practice of increasing resolution. • Power-based scheduling results in less reserve activation at equal computation time. • Increasing the resolution requires great computational expense. • We provide a set of market rules to implement and price power-based bids. Abstract Day-ahead electricity markets are inefficient due to their coarse discretisation of time and their representation of electricity production and consumption in energy per time interval. This leads to excessive costs and infeasible schedules in the market clearing results. Some real-world systems have increased the resolution to improve accuracy, but this comes at a high computational cost. We propose an alternative, based on using linear power trajectories in the day-ahead scheduling process, which represent the momentary electricity production. Changing from a traditional energy-based to such a power-based formulation of the scheduling method can reduce cost by several percentage points, leading to a cost reduction of millions of euros in real-world systems on a yearly basis. Attempting to do so by increasing the resolution of the schedule would be accompanied by large increases in computational demands. Furthermore, we provide market design options to implement power-based bidding and pricing in day-ahead electricity markets, showing that pricing and market rules which encompass existing markets are readily available for implementation, and illustrate these with examples. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy resources into the power system is rising prominently worldwide, and is causing increased uncertainty in power system operations. Increasing loads and scarce transmission resources have exacerbated this issue, imposing critical operational challenges, such as congestion issues, on the power system operator (SO). This study proposes a unit commitment that employs dynamic line rating (DLR) to develop a method that enables efficient use of network resources. A mathematical formulation is suggested, based on two-stage stochastic programming, to manage the newly introduced uncertainties related to DLR properly. In addition, we build approximated power-flow equations for the proposed model. The performance of the proposed method is verified using a six-bus system and the modified IEEE 118-bus system integrated with wind power units. The simulation results clearly demonstrate that, with DLR, an SO can significantly reduce the expected operating cost. This advantage stems from the sufficiently effective utilization of the existing transmission line, which leads to the optimal commitment of the generating units and dispatched volume, and efficient use of the harvested wind energy. Moreover, the results, which demonstrate that the expected-energy-not-supplied is markedly reduced by incorporating the DLR, prove that the proposed method is highly reliable. [ABSTRACT FROM AUTHOR]

Short term probabilistic load forecasting is essential for any power generating utility. This paper discusses an application of partially linear additive quantile regression models for predicting short term electricity demand during the peak demand hours (i.e. from 18:00 to 20:00) using South African data for January 2009 to June 2012. Additionally the bounded variable mixed integer linear programming technique is used on the forecasts obtained in order to find an optimal number of units to commit (switch on or off. Variable selection is done using the least absolute shrinkage and selection operator. Results from the unit commitment problem show that it is very costly to use gas fired generating units. These were not selected as part of the optimal solution. It is shown that the optimal solutions based on median forecasts ( Q 0.5 quantile forecasts) are the same as those from the 99th quantile forecasts except for generating unit g 8 c , which is a coal fired unit. This shows that for any increase in demand above the median quantile forecasts it will be economical to increase the generation of electricity from generating unit g 8 c . The main contribution of this study is in the use of nonlinear trend variables and the combining of forecasting with the unit commitment problem. The study should be useful to system operators in power utility companies in the unit commitment scheduling and dispatching of electricity at a minimal cost particularly during the peak period when the grid is constrained due to increased demand for electricity. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven affinely adjustable distributionally robust method for unit commitment considering uncertain load and renewable generation forecasting errors. The proposed formulation minimizes expected total operation costs, including the costs of generation, reserve, wind curtailment, and load shedding, while guaranteeing the system security. Without any presumption about the probability distribution of the uncertainties, the proposed method constructs an ambiguity set of distributions using historical data and immunizes the operation strategies against the worst case distribution in the ambiguity set. The more historical data is available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the amount of historical data increases. Numerical results and Monte Carlo simulations on the 118- and 1888-bus systems demonstrate the favorable features of the proposed method. [ABSTRACT FROM AUTHOR]

This paper aims at providing a novel techno-economic flexibility index to quantify the relative flexibility of different conventional generation technologies. The proposed measure takes into account a comprehensive set of technical flexibility characteristics of generation units including minimum stable generation level, operating range, minimum up/down times, and ramp up/down capability. The impact of various technical parameters on the flexibility restriction is determined based on the additional costs imposed to the system operator in order to satisfy that constraint. Afterward, the calculated flexibility index is incorporated into day-ahead market clearing procedure as a new objective function beside operation cost using a multiobjective decision-making approach. Simulation results show that the need of different flexibility levels changes the optimal generation dispatch and results in different operation costs. Furthermore, the impact of two emerging flexible options, i.e., bulk energy storages and a typical demand response program in the flexibility contribution of other generation technologies is also investigated. The results reveal that the integration of emerging flexible options frequently changes the flexibility share of conventional units and can provide the required flexibility level at a lower cost. [ABSTRACT FROM AUTHOR]

Part one of this two-part paper presents new models for evaluating flexible resources in two-settlement electricity markets (day-ahead and real-time) with uncertain net loads (demand minus wind). Physical resources include wind together with fast- and slow-start demand response and thermal generators. We also model financial participants (virtual bidders). Wind is stochastic, represented by a set of scenarios. The two-settlement system is modeled as a two-stage process in which the first stage involves unit commitment and tentative scheduling, while the second stage adjusts flexible resources to resolve imbalances. The value of various flexible resources is evaluated through four two-settlement models: 1) an equilibrium model in which each player independently schedules its generation or purchases to maximize expected profit; 2) a benchmark (expected system cost minimization); 3) a sequential equilibrium model in which the independent system operator first optimizes against a deterministic wind power forecast; and 4) an extended sequential equilibrium model with self-scheduling by profit-maximizing slow-start generators. A tight convexified unit commitment allows for demonstration of certain equivalencies of the four models. We show how virtual bidding enhances market performance, since, together with self-scheduling by slow-start generators, it can help a deterministic day-ahead market to choose the most efficient unit commitment. [ABSTRACT FROM PUBLISHER]

Short-term hydro-generation planning can be efficiently modeled as a mixed integer linear program (MILP). Depending on the size of the system and the time horizon, the resulting MILP may be too large to be solved in reasonable time with commercial solvers. This paper presents a three-phase approach based on price decomposition that yields quickly near-optimal solutions to large-scale real-world instances. For any partition of the production system into subsystems, the first phase solves a linear program to estimate the marginal cost of electricity in each subsystem. The second phase solves local MILPs corresponding to each subsystem, and gives a solution that is almost feasible. The final phase slightly perturbs the solution to obtain a feasible solution that is proven to be near-optimal. Our method is tested on real instances corresponding to Hydro-Québec's production system. [ABSTRACT FROM PUBLISHER]

In this paper, we present the state-transition formulation for the unit commitment (UC) problem. This formulation uses new decision variables that capture the state transitions of the generators, instead of their on/off statuses. We show that this new approach produces a formulation which naturally includes valid inequalities, commonly used to strengthen other formulations. We demonstrate the performance of the state-transition formulation and observe that it leads to improved solution times especially in longer time-horizon instances. As an important consequence, the new formulation allows us to solve realistic instances in less than 12 minutes on an ordinary desktop PC, leading to a speed-up of a factor of almost two, in comparison to the nearest contender. Finally, we demonstrate the value of considering longer planning horizons in UC problems. [ABSTRACT FROM PUBLISHER]

Security-constrained unit commitment (SCUC) problem is one of the necessary tools for system operators to make operational planning and real-time operation. The internalization of transmission network and security constraints (e.g., N-1 criterion) could lead to different decisions in the generation dispatch. However, the computational burden of this problem is challenging mainly due to its inherent large problem size. Therefore, this paper proposes an N-1 security constrained formulation based on the line outage distribution factors (LODF) instead of the one based on injection sensitivity factors (ISF). This formulation is at the same time more compact than analogous formulations for contingency constraints; hence, it presents a lower computational burden. The computational efficiency of the proposed formulation is shown by solving the SCUC of the IEEE 118 bus system with LODF and ISF. Additionally, an iterative methodology for filtering the active N-1 congestion constraints is detailed, and its implementation for large-scale systems is described. The results show that the proposed filter reduces the computational time by approximately 70% in comparison to the complete formulation of N-1 constraints in SCUC. [ABSTRACT FROM PUBLISHER]

This paper provides a reformulation of the scenario-based two-stage unit commitment problem under uncertainty that allows finding unit-commitment plans that perform reasonably well both in expectation and for the worst case. The proposed reformulation is based on partitioning the sample space of the uncertain factors by clustering the scenarios that approximate their probability distributions. The degree of conservatism of the resulting unit-commitment plan (that is, how close it is to the one provided by a purely robust or stochastic unit-commitment formulation) is controlled by the number of partitions into which the said sample space is split. To efficiently solve the proposed reformulation of the unit-commitment problem under uncertainty, we develop two alternative parallelization and decomposition schemes that rely on a column-and-constraint generation procedure. Finally, we analyze the quality of the solutions provided by this reformulation for a case study based on the IEEE 14-node power system and test the effectiveness of the proposed parallelization and decomposition solution approaches on the larger IEEE 3-Area RTS-96 power system. [ABSTRACT FROM PUBLISHER]

Unit commitment is a traditional mixed-integer non-convex problem and remains a key optimisation task in power system scheduling. The high penetration of intermittent renewable generations such as wind and solar as well as mass roll-out of plug-in electric vehicles (PEVs) impose significant challenges to the traditional unit commitment problem, not only by significantly increasing the complexity of the problem in terms of the dimension and constraints, but also dramatically change the problem formulation. In this paper, a new hybrid unit commitment problem considering renewable generation scenarios and charging and discharging management of plug-in electric vehicles is first formulated. To effectively solve the problem, a novel parallel-series hybrid meta-heuristic optimisation method is then proposed, which combines a hybrid topology binary particle swarm optimisation, the self-adaptive differential evolution algorithm and a lambda iteration method, to simultaneously and intelligently determine the binary on/off status of each thermal unit, the generation power of online units, as well as the demand side management of plug-in electric vehicles. The proposed parallel-series hybrid method is first assessed on a 10-unit benchmark, and then on a case where renewable generation and smart PEV management are integrated. Numerical results confirm the superiority of the proposed new algorithm in comparison with some popular meta-heuristic approaches. [ABSTRACT FROM AUTHOR]

The unit commitment (UC) problem aims to find an optimal schedule of generating units subject to demand and operating constraints for an electricity grid. The majority of existing algorithms for the UC problem rely on solving a series of convex relaxations by means of branch-and-bound and cutting-planning methods. The objective of this paper is to obtain a convex model of polynomial size for practical instances of the UC problem. To this end, we develop a convex conic relaxation of the UC problem, referred to as a strengthened semidefinite program (SDP) relaxation. This approach is based on first deriving certain valid quadratic constraints and then relaxing them to linear matrix inequalities. These valid inequalities are obtained by the multiplication of the linear constraints of the UC problem, such as the flow constraints of two different lines. The performance of the proposed convex relaxation is evaluated on several hard instances of the UC problem. For most of the instances, globally optimal integer solutions are obtained by solving a single convex problem. For the cases where the strengthened SDP does not give rise to a global integer solution, we incorporate other valid inequalities. The major benefit of the proposed method compared to the existing techniques is threefold: (i) the proposed formulation is a single convex model with polynomial size and, hence, its global minimum can be found efficiently using well-established first-and second-order methods by starting from any arbitrary initial state, (ii) unlike heuristic methods and local-search algorithms that return local minima whose closeness to a global solution cannot be measured efficiently, the proposed formulation aims at obtaining global minima, (iii) the proposed convex model can be used in convex-hull pricing to minimize uplift payments made to generating units in energy markets. The proposed technique is extensively tested on IEEE 9-bus, IEEE 14-bus, IEEE 30-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus systems with different settings and over various time horizons. [ABSTRACT FROM AUTHOR]

In this work, we develop a mathematical model and framework to represent rolling-horizon unit commitment (UC) processes with multiple periodicities. In control center operations, UC is solved repeatedly to adjust device commands based on new information about load, generation availability, renewable energy production, and other aspects of system state as uncertain conditions are realized. We develop a three-level model including 24-h UC, rolling-horizon UC (RHUC), and economic dispatch (ED) and give formulations for the three problems including interdependencies. This framework allows for evaluation of, among other things, different periodicities of the rolling horizon problem and the benefits of more accurate forecasting information. Experimental results are shown for a 6-bus system and a 3012-bus system with wind generation in two wind scenarios. Although the generation costs are very similar, the deviation between RHUC schedules and actual deployment is noted to be superior for a 20-min periodicity compared to a 30-min periodicity. Additionally, less reserve is deployed in the 20-min RHUC case. [ABSTRACT FROM AUTHOR]

Consider a general security-constrained unit commitment (SCUC) problem for an arbitrary power network. This problem includes discrete variables corresponding to commitment parameters as well as demand and generation constraints, among others. Aside from its nonconvexity, SCUC is a large-scale problem for real-world systems due to the security constraints. The main objective of this paper is to propose an algorithm to eliminate a vast majority of linear security constraints in the high-dimensional mixed-integer SCUC problem in order to arrive at an equivalent reduced-order SCUC problem. To this end, we develop a parallel and computationally cheap algorithm for finding a minimal subset of security constraints whose satisfaction guarantees the satisfaction of all security constraints. The proposed algorithm does not depend on the unknown unit commitment parameters and allows the load forecasts to be imprecise. More specifically, a low-order model of the SCUC problem is found based on the topology of the power system, given lower and upper bounds on nodal power injections (to accommodate uncertainties in loads and generation productions), and the normal and emergency line ratings. This algorithm is tested on several power systems with as many as 5500 buses, for which each set of security constraints with millions of conditions is reduced to a minimal subset with only a few hundred conditions. [ABSTRACT FROM AUTHOR]

Unit Commitment (UC) is an optimization problem used to determine the operation schedule of the generating units at every hour interval with varying loads and generations under different generational, environmental and technical constraints. With the significant increase of Renewable Energy Sources (RES) integration into the power networks, effects posed by these system changes to the UC are actively being studied and investigated by global researchers and operation engineers. To this end, this paper firstly provides a literature survey of UC concept, objectives and constraints. Different UC models developed for addressing RES impacts are also reviewed. Moreover, many algorithms have been proposed in the past few decades to optimize the UC problem. This work explores the necessity for alternative optimization approaches for UC solution. In doing that, the work uncovers the advantages and disadvantages of the existing methodologies so that future algorithms could be designed in retaining the advantages of the existing methodologies while avoiding the presented weaknesses. In addition, installation of energy storage devices to balance the fluctuation in power generation and their associated impacts on UC models are reviewed. The contents of this paper provide ready-to-refer and ready-to-use information for the researchers working in the field of UC. [ABSTRACT FROM AUTHOR]

This paper discusses the modeling and analysis of a carbon capture technology-based resource scheduling/unit commitment (UC) methodology in carbon/emission-constrained environment. In carbon markets, the overall generation from conventional/fossil-fueled thermal plants is constrained over the total cost which is the sum of generation, capture cost/emission avoidance cost. Therefore, in this paper, generalized/uniform performance indices affecting the UC schedule are derived. Also, a commitment/scheduling methodology based on capture and fuel cost is devised rather than the existing penalty cost methodology as in the modernized markets everything was dealt in monetary value. Furthermore, the impact of type of resource used, i.e., coal rank is also considered to evaluate the sensitivity of scheduling decisions and financial influence of carbon capture technology. Along with performance indices, correction factors are also proposed to justify the effect of resource/coal rank in the process of optimal generation allocation. The effect of correction factors and type of coal is observed to be predominant at lower capture efficiencies compared to higher capture efficiency. The effectiveness of the proposed method over the penalty method is reflected in reduced generation cost and emission avoidance cost when compared to the penalty-based methodology. [ABSTRACT FROM AUTHOR]

A great share of wind power generation in a power system is likely to test the system flexibility with extreme power ramp events. Therefore, this paper proposes a framework that allows the assessment of electricity demand flexibility on these events. Two electric heating methods are of particular interest, since heating is an attractive source of flexibility in a cold climate. The proposed framework consists of generating wind generation scenarios, identifying severe ramps, and solving a system unit commitment (UC) problem, with and without demand response (DR), when these ramps occur. These stages are integrated into a Monte Carlo simulation technique, which allows the capturing of the electric heating load contribution to mitigate ramp effects in different system conditions. The contribution is investigated in case studies and evaluated with several measures. [ABSTRACT FROM AUTHOR]

In recent years, the share of renewable energy sources (RES) in the electricity generation mix has been expanding rapidly. However, limited predictability of the RES poses challenges for traditional scheduling and dispatching mechanisms based on unit commitment (UC) and economic dispatch (ED). This paper presents an advanced UC-ED model to incorporate wind generators as RES-based units alongside conventional centralized generators. In the proposed UC-ED model, an imbalance cost is introduced reflecting the wind generation uncertainty along with the marginal generation cost. The proposed UC-ED model aims to utilize the flexibility of fleets of plug-in electric vehicles (PEVs) to optimally compensate for the wind generation uncertainty. A case study with 15 conventional units and 3 wind farms along with a fixed-sized PEV fleet demonstrates that shifting of PEV fleets charging at times of high wind availability realizes generation cost savings. Nevertheless, the operational cost saving incurred by controlled charging appears to diminish when dispatched wind energy becomes considerably larger than the charging energy of PEV fleets. Further analysis of the results reveals that the effectiveness of PEV control strategy in terms of CO 2 emission reduction is strongly coupled with generation mix and the proposed control strategy is favored in cases where less pollutant-based plants like nuclear and hydro power are profoundly dominant. [ABSTRACT FROM AUTHOR]

Symmetry is a natural phenomenon that appears when an electric system has identical generating units. As a core function in any electricity market, the unit commitment algorithm solves a very large-scale mixed-integer based optimization problem. In this work, the symmetric properties that exist in a competitive electricity market are exploited in improving the performance of its unit commitment solutions. A static symmetry removal strategy based on hierarchical constraints is proposed. They establish priorities for the unit status variables. Extensive numerical examples are illustrated using a real-world, large-scale electricity market. Two available commercial solvers are used to compare the performance of each solver with and without our proposed method. Results show that there are opportunities to improve the unit commitment solutions if the symmetric properties of the market system are properly exploited. [ABSTRACT FROM AUTHOR]

This paper presents a generic Mixed Integer Linear Programming (MILP) model that integrates a Mid-term Energy Planning (MEP) model, which implements generation and transmission system planning at a yearly level, with a Unit Commitment (UC) model, which performs the simulation of the Day-Ahead Electricity Market. The applicability of the proposed model is illustrated in a case study of the Greek interconnected power system. The aim is to evaluate a critical project in the Ten Year Network Development Plan (TYNDP) of the Independent Power Transmission System Operator S.A. (ADMIE), namely the electric interconnection of the Crete Island with the mainland electric system. The proposed modeling framework identifies the implementation (or not) of the interconnection of the Crete Island with the mainland electric system, as well as the optimum interconnection capacity. It also quantifies the effects on the Day-Ahead electricity market and on the energy mix. The paper demonstrates that the model can provide useful insights into the strategic and challenging decisions to be determined by investors and/or policy makers at a national and/or regional level, by providing the optimal energy roadmap and management, as well as clear price signals on critical energy projects under real operating and design constraints. [ABSTRACT FROM AUTHOR]

The use of appropriate hourly reserve margins can maintain power system security by balancing supply and demand in the presence of errors in the forecast demand, generation outages, or errors in the forecast of wind power generation. Because the cost of unit commitment increases with larger reserve margins, cost analysis to determine the most economical reserve margin is an important issue in power system operation. Here, we define the "short-term reliability of balance" and describe a method to determine the reserve margin based on the short-term reliability of balance. We describe a case study, in which we calculate the reserve margin using this method with various standards of short-term reliability of balance. A cost analysis is then performed to determine the most economic standard, and a comparison between our method and a conventional method is carried out. The results show that our method with an economic short-term reliability of balance enables more reliable and efficient operation of the power system. Moreover, with an hourly reserve margin, we show that an increase in wind power generation can result in a significant decrease in the operating cost, which makes wind power generation economically viable. [ABSTRACT FROM AUTHOR]

This paper proposes an improved firefly (FF) algorithm with multiple workers for solving the unit commitment (UC) problem of power systems. The UC problem is a combinatorial optimization problem that can be posed as minimizing a quadratic objective function under system and unit constraints. Nowadays, highly developed computer systems are available in plenty, and proper utilization of these systems will reduce the time and complexity of combinatorial optimization problems with large numbers of generating units. Here, multiple workers are assigned to solve a UC problem as well as the subproblem, namely economic dispatch (ED) in distributed memory models. The proposed method incorporates a group search in a FF algorithm and thereby a global search is attained through the local search performed by the individual workers, which fine tune the search space in achieving the final solution. The execution time taken by the processor and the solution obtained with respect to the number of processors in a cluster are thoroughly discussed for different test systems. The methodology is validated on a 100 unit system, an IEEE 118 bus system, and a practical Taiwan 38 bus power system and the results are compared with the available literature. [ABSTRACT FROM AUTHOR]

Classic unit commitment is the important and challenging task of allocating generating units subject to basic constraints over a scheduled time horizon to obtain the least generation cost. Penetration of distributed energy resources in modern power systems makes generation planning more complex. This article presents the individual and combined effect of three distributed energy resources, namely wind power generator as a renewable energy source, plug-in electric vehicles, and emergency demand response program on unit commitment. The inconsistent nature of wind speed and wind power is characterized by the Weibull probability distribution function considering overestimation and underestimation costs of stochastic wind power. The comprehensive comparative analysis of the economic viability on unit commitment is carried out to minimize the total cost of the entire system. To obtain the optimum solution, a modified teaching–learning-based optimization algorithm is used. The IEEE standard ten-unit test system is used for this study. To validate the efficacy of the modified teaching–learning-based optimization algorithm, a 26-unit reliability test system is also considered. It is found that the collective effect of wind power generator, plug-in electric vehicles, and emergency demand response program on unit commitment provides significant reduction in the total cost. [ABSTRACT FROM PUBLISHER]

The high penetration of renewables envisaged for future power systems will significantly increase the need for flexible operational measures and generation technologies, whose associated investment decisions must be properly planned in the long term. To achieve this, expansion models will need to incorporate unit commitment constraints, which can result in large scale MILP problems that require significant computational resources to be solved. In this context, this paper proposes a novel Dantzig–Wolfe decomposition and a column generation approach to reduce the computational burden and overcome intractability. We demonstrate through multiple case studies that the proposed approach outperforms direct application of commercial solvers, significantly reducing both computational times and memory usage. Using the Chilean power system as a reference case, we also confirm and highlight the importance of considering unit commitment constraints in generation expansion models. [ABSTRACT FROM AUTHOR]

The unit commitment problem (UCP) for thermal units consists of finding an optimal electricity production plan for a given time horizon. In this paper we propose hybrid approaches which combine Variable Neighborhood Search (VNS) metaheuristic and mathematical programming to solve this NP-hard problem. Four new VNS based methods, including one with adaptive choice of neighborhood order used within deterministic exploration of neighborhoods, are proposed. A convex economic dispatch subproblem is solved by Lambda iteration method in each time period. Extensive computational experiments are performed on well-known test instances from the literature as well as on new large instances generated by us. It appears that the proposed heuristics successfully solve both small and large scale problems. Moreover, they outperform other well-known heuristics that can be considered as the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper is the first of a two part study that considers long term, least cost, GHG (greenhouse gas) abatement pathways for an electricity system. Part 1 formulates a planning model to optimise these pathways and presents results for a single reference scenario. Part 2 applies this model to different scenarios and considers the policy implications. The planning model formulated has several constraints which are important when considering GHG abatement and widespread uptake of intermittent renewable generation. These constraints do not appear to have been integrated into a single planning model previously, and include constraints on annual GHG emissions, unit commitment, storage, plant dynamics and intermittent renewable generation. The model prioritises overall abatement, and therefore does not include a price on carbon or support for any particular technology. The model is applied to Australia's NEM (National Electricity Market) as an example. All model inputs – for technologies, demand, and meteorological data – are from the most current and authoritative public sources. As such, the results are transparently derived and both policy and technology neutral. For the reference scenario presented here, key technologies are wind from 2015, gas generation from 2030, and solar generation from 2040. [ABSTRACT FROM AUTHOR]

Small-scale power systems mainly present two particular problems when it comes to their modeling and solution. Due to their size, it is important to consider and meet various types of reserve requirements in order to have a reliable operation of the power system. It is also important to consider variable start-up costs in order to obtain a more accurate unit commitment. In this paper six different types of reserve requirements are considered for the unit commitment problem: spinning reserve, regulation reserve (AGC per unit), ten-minute reserve, ten-minute non-synchronized reserve, ten-minute operational reserve, and ten-minute distributed reserve. Additionally, a Mixed Integer Programming formulation is introduced to represent variable start-up costs. The model introduced here is currently in use by the Baja California (México) power system operator. [ABSTRACT FROM AUTHOR]

This article proposes the use of a heuristic method based on the ant colony optimization algorithm (MACO) to solve the dynamic economic dispatch (DED) problem with taking into consideration the valve-point effects. The suggested method takes into account both static and dynamic constraints of generator operation, the transmission losses and non-smooth cost functions that shape the valve-point effects. Also, a strategy for the DED imposed equality constraint handling is suggested. The proposed method's feasibility is proven for three different systems that have 10, 13 and 30 thermal generating units. The simulation results show that the suggested approach is very efficient having a capacity to obtain quality solutions in solving the DED problem with valve-points effects. The numerical results obtained through the suggested method are superior to other results recently reported in literature, for the same systems. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to solve the unit commitment (UC) problem by solving a sequence of increasingly tight continuous relaxations based on the techniques of reformulation and lift-and-project (L&P). After projecting the power output of unit onto [0, 1], the continuous relaxation of the UC problem can be tightened with the reformulation techniques. Then, a tighter model which is called L&P-TMIP is established by strengthening the continuous relaxation of the feasible region of the tight UC problem iteratively using L&P. High-quality suboptimal solutions can be obtained from the solutions of the relaxation for this tight model. The simulation results for realistic instances that range in size from 10 to 200 units over a scheduling period of 24 h show that the proposed tight relaxation method is competitive with general-purpose mixed integer programming solvers based methods for large-scale UC problems due to the excellent performance and the good quality of the solutions it generates. [ABSTRACT FROM PUBLISHER]

To handle significant variability in loads, renewable energy generation, as well as various contingencies, (two-stage) robust optimization method has been adopted to construct unit commitment models and to ensure reliable solutions. In this paper, we further explore and extend the modeling capacity of two-stage robust optimization and present two new robust unit commitment variants: the expanded robust unit commitment and the risk constrained robust unit commitment model. We derive structural properties, demonstrate the connection to the popular scenario based stochastic unit commitment models, and present a customized column-and-constraint generation method. Numerical experiments on those models are performed using practical data sets, which illustrate their modeling strength, economic outcomes, and the algorithm performance in solving those models. [ABSTRACT FROM PUBLISHER]

Optimal operation of power systems with high integration of renewable power sources has become difficult as a consequence of the random nature of some sources like wind energy and photovoltaic energy. Nowadays this problem is solved using the Monte Carlo Simulation (MCS) approach, which allows the consideration of important statistical characteristics of wind and solar power production, such as the correlation between consecutive observations, the diurnal profile of the forecasted power production, and the forecasting error. In this paper, a new model of the unit scheduling of power systems with significant renewable power generation based on the scenario generation/reduction method combined with the priority list (PL) method is proposed that finds the probability distribution function (PDF) of a determined generator be committed or not. This approach allows the recognition of the role of each generation unit on the day-ahead unit commitment (UC) problem with a probabilistic point of view, which is important for acquiring a cost-effective and reliable solution. The capabilities and performance of the proposed approach are illustrated through the analysis of a study case, where the spinning reserve requirements are probabilistically verified with success. [ABSTRACT FROM AUTHOR]

The goal of the short-term unit commitment is the minimization of the total operation cost while satisfying all unit and system constraints. One of the main issues while solving the unit commitment optimization problem is the planning of the capacity reserves of the power system. In order to address this issue, a dynamic method for probabilistic assessment of generation unavailability is proposed within this paper. The main highlight feature of this method is that it has the capacity to account for the unavailability implications of the generating unit states, being committed or decommitted as well as their start-up characteristics. This allows more comprehensive hour-to-hour scheduling analyses from the aspect of probabilistic unavailability assessment. The generating capacities unavailability is designated as the relevant unavailability measure regarding the power supply to loads. The unit commitment problem is developed as a multi-objective optimization problem. Two objective functions are considered: the total operating cost of the generating capacities as one and generating capacities unavailability as the other objective function. An improved hybrid genetic algorithm is applied for solving the problem. A test power system is used as a case study. The obtained results indicate the need and benefits of more detailed modelling of the power generation availability. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage stochastic unit commitment (UC) model, which integrates non-generation resources such as demand response (DR) and energy storage (ES) while including risk constraints to balance between cost and system reliability due to the fluctuation of variable generation such as wind and solar power. This paper uses conditional value-at-risk (CVaR) measures to model risks associated with the decisions in a stochastic environment. In contrast to chance-constrained models requiring extra binary variables, risk constraints based on CVaR only involve linear constraints and continuous variables, making it more computationally attractive. The proposed models with risk constraints are able to avoid over-conservative solutions but still ensure system reliability represented by loss of loads. Then numerical experiments are conducted to study the effects of non-generation resources on generator schedules and the difference of total expected generation costs with risk consideration. Sensitivity analysis based on reliability parameters is also performed to test the decision preferences of confidence levels and load-shedding loss allowances on generation cost reduction. [ABSTRACT FROM AUTHOR]

The ability of load to respond to short-term variations in electricity prices plays an increasingly important role in balancing short-term supply and demand, especially during peak periods and in dealing with fluctuations in renewable energy supplies. However, price responsive load has not been included in standard models for defining the optimal scheduling of generation units in short-term. Here, elasticities are included to adjust the demand profile in response to price changes, including cross-price elasticities that account for load shifts among hours. The resulting peak reductions and valley fill alter the optimal unit commitment. Enhancing demand response also increases the amount of wind power that can be economically injected. Further, wind power uncertainty can be managed at a lower cost by adjusting electricity consumption in case of wind forecast errors, which is another way in which demand response facilitates the integration of intermittent renewables. [ABSTRACT FROM AUTHOR]

Since many years, the electricity industry is being dominated by vertically integrated utilities. The shakeup has imposed the disintegration of the three electric power industry components: generation, transmission and distribution. The movement of the electricity industry environment into a distributed industry environment has made the market forces to drive the electricity price and lessen the net cost through amplified competition. The competitive electricity market environment necessitates wide access to transmission and distribution networks that connect scattered electricity suppliers and customers. Regardless of the fact that transmission charges represent a minor proportion of overall operating expenses in utilities in competitive electricity markets, transmission pricing should be a reasonable cost-effective pointer used by the energy market to make decisions on source provision, system enlargement and reinforcement. The operating cost of a transmission transaction diminishes if it reduces the cost of generation. The wheeling cost can be decreased by reducing generation cost via improving generation dispatch due to either lower losses or mitigation of operating constraints, via involving generation scheduling. This paper introduces the concepts of both electricity market and power flow tracing methods, and provides a detailed presentation of both Bialek's tracing method and an embedded wheeling cost methodology namely 'Line-by-line' methodology. The per unit cost, generation cost and wheeling cost are calculated and compared before and after the application of optimal power dispatch involving generation scheduling with the objective of minimization of all the aforementioned three costs, to an application example illustrated. [ABSTRACT FROM AUTHOR]

In the current design of U.S. electricity spot markets, the generation dispatch mechanism and the payment rule are incompatible with each other. The uniform-price auction format predicated on two-sided market design with marginal pricing is flawed, since the supply and the demand are not treated equitably and discrete decisions, such as unit commitment, are inevitable. The pay-as-bid scheme is better suited for the market reality. [Copyright &y& Elsevier]

Abstract: This paper addresses the self-scheduling problem of generation companies owning thermal power units considering bilateral contracts and day-ahead market. This approach allows precise modelling of variable costs, start-up costs and comprehensive system of constraints. The self-scheduling model is formulated as deterministic optimization problem in which expected profit is maximized by 0/1 mixed-integer linear programming technique. Solution is achieved using the homogeneous and self-dual interior point method for linear programming, with a branch and bound optimizer for integer programming. The effectiveness of the proposed model for optimizing the thermal generation schedule is demonstrated through the case study with detailed discussion. [Copyright &y& Elsevier]